AI-Based Method for Detecting Retinal Haemorrhage in Eyes with Malarial Retinopathy

Abstract

Cerebral Malaria (CM) as one of the most common and severe diseases in sub-Saharan Africa, claimed the lives of more than 435,000 people each year. Because Malarial Retinopathy (MR) is as one of the best clinical diagnostic indicators of CM, it may be essential to analysing MR in fundus images for assisting the CM diagnosis as an applicable solution in developing countries. Image segmentation is an essential topic in medical imaging analysis and is widely developed and improved for clinic study. In this paper, we aim to develop an automatic and fast approach to detect/segment MR haemorrhages in colour fundus images. We introduce a deep learning-based haemorrhages detection of MR inspired by Dense-Net based network called one-hundred-layers tiramisu for the segmentation tasks. We evaluate our approach on one MR dataset of 259 annotated colour fundus images. For keeping the originality of raw MR colour fundus images, 6,098 sub-images are extracted and split into a training set (70%), a validation set (10%) and a testing set (20%). After implementation, our experimental results testing on 1,669 annotated sub-images, show that the proposed method outperforms commonly mainstream network architecture U-Net.